Hacker News

Article URL: https://4playerchess.plus

Comments URL: https://news.ycombinator.com/item?id=48979496

Points: 1

# Comments: 1

Article URL: https://magazine.sebastianraschka.com/p/controlling-reasoning-effort-in-llms

Comments URL: https://news.ycombinator.com/item?id=48979475

Points: 2

# Comments: 0

I am a frontend engineer and actively use AI. I caught myself that i never write code manually, only if i have to tweak some styling. I feel lazy and dumb compared to pre-AI era. Even if i have to write some small changes in code that take like 2 minutes - it is easier to spend those 2 minutes explaining everything in details to AI and point out what and how it needs to change. Because i dont have to think about the process, i only explain the idea and desired solution - then wait until its done to review.

Thats some kind of degradation. But the thing is that the quality is amazing. My productivity and speed increased significantly.

But there is a frustrating thing. The laziness started affect even review process. I feel like i dont want to spend time to read and check all the changes - i will better ask another AI to do it. And it works! I use Claude for like 95% of work, then switch to GPT to review everything properly. They have different personalities so you have different points of view. I consider GPT as a nerd - it catches a lot of gaps and inconsistencies that Claude misses. So i like to close them both in one room to debate.

What i really do care a lot is a comprehensive documentation. I spend a huge amount of time for planning and brainstorming with AI, then i document everything properly. All those MD files about architecture, composition recipes, cheat sheet… All of it should lie on surface, not to be buried somewhere deep in code. Every cold session starts with all the docs where everything is explained properly with references and examples to prevent AI to skim or give lazy answers. When i need to implement some big feature i structure a proper ready-to-execute plan so every next agent with fresh context reads it - analyzes what was done - figures out what is the task for this session - and goes ahead with all the changes, then report. It can be dozens of chats working sequentially on some big thing; in the end i will run some workflow to review all the implementation, then ask GPT to audit changes, and i will look myself only when AI approves that everything looks great. Then i can tell what and why has to be redone because i dont like how we have it now.

In my setup the documentation for AI outweighs the code (instruction files on every level, rule files that load by file type, subagents with roles, hooks around every edit). The most weightful part:

- A catalog of silent failures. 36 numbered traps that compile fine but render broken. Every entry is a real failure that shipped at least once: cause, symptom, fix.

- A "polish" rule. When you tell AI "make it better", its reflex is to ADD decorative stuff. So there is a written rule that polish means subtraction, with a list of patterns i rejected in real reviews.

- CI that fails when docs drift from code. The component registry is checked against source files. Because docs that lie are worse than no docs.

- A runtime verifier (WebMCP). An agent drives a real Chrome browser, clicks through the page, watches the console, takes screenshots at 3 viewports.

Basically I stopped writing code and started engineering the environment that writes the code. Every file exists because AI failed at something once, and i wrote it down so it wont happen again.

The biggest problem for me is motivation. I remember that feeling how you got stuck - cant figure out something for hours, searching right solution, trying, and then FUCK YEAH FINALLY, and the satisfaction: YOU NAILED IT. I dont have it anymore. I feel confident even in things where i have no expertise at all. Thats like: meh AI can handle it.

I think my system solves the quality question, but increases the laziness/motivation problem. I made it safe to be lazy.

So two questions:

1. How does your AI setup look like? I mean real files and rules, not just how you prompt.

2. Has anyone actually solved the motivation part?

Comments URL: https://news.ycombinator.com/item?id=48979474

Points: 2

# Comments: 0

Article URL: https://bartoszmilewski.com/2026/07/19/profunctor-optics/

Comments URL: https://news.ycombinator.com/item?id=48979470

Points: 1

# Comments: 0

Article URL: https://theconsensus.dev/p/2026/07/11/of-forks-and-clones-and-package-management.html

Comments URL: https://news.ycombinator.com/item?id=48979469

Points: 1

# Comments: 0

Article URL: https://www.reuters.com/business/world-at-work/asml-offer-employees-20000-retention-bonus-staying-2027-2030-2026-07-20/

Comments URL: https://news.ycombinator.com/item?id=48979461

Points: 1

# Comments: 0

Article URL: https://www.reuters.com/business/healthcare-pharmaceuticals/fda-says-taylor-farms-cyclospora-finding-was-false-positive-2026-07-20/

Comments URL: https://news.ycombinator.com/item?id=48979432

Points: 1

# Comments: 0

Article URL: https://worldholidays.me

Comments URL: https://news.ycombinator.com/item?id=48979428

Points: 1

# Comments: 0

I feel like from day one, LLMs was talked about in relation to how it will change the world, how it's good or bad for business and the environment. But then yesterday, I was out walking with the dog, and it hit me how freaking cool this technology is.

I don't want to talk about where I am on the spectrum between booster and denier. I'm so sick and tired of hearing about it, and it doesn't matter in this context.

I spent the first 8 years of my career trying to make the computer understand reality through images - scanning receipts, finding defects on wind turbine blades, and other niche-but-important things. The amount of work it took to just read a receipt and get the text somewhat right.. It was insane. It was really fun work, but it was so hard to make the real world fit into boxes.

I feel like I completely missed being excited about LLMs - instead I got dragged into discussions about what it means. And it's not because I've lost the ability to care; I can get excited about a new hashing algorithm and other weird tech, but LLMs? it didn't even register as a technology. Maybe it's an uncanny valley kind of thing - where it doesn't look like a classic computer science thing, so my brain didn't register it as such?

During my walk I started thinking of the possibilities. And I know your mind might immediately jump to fully-fledged big-scope solutions, but just try to give it space in that other part of the mind - the part where it's cool and not a tool to change our society. We have no idea what's gonna happen - and that's fine - we've never known that. But what we do know is that we have this really cool technology that you can run on your own computer now.

You can actually literally IRL for real, make a python program yourself that you can talk to and it understands you and do things. All on your own machine. No nothing! IT'S LIKE A TOTALLY NORMAL TECHNOLOGY! It's so cool, I can't really believe it. It can even view images and explain it you, and I just.. I'm so excited now.

Don't let them take the fun out of this technology, and spend a few seconds thinking about that cool little trick that made it possible for us to talk to sand.

Comments URL: https://news.ycombinator.com/item?id=48979417

Points: 1

# Comments: 0

Article URL: https://www.wsj.com/world/europe/stagnant-germany-considers-the-unthinkable-sunday-shopping-13b4651c

Comments URL: https://news.ycombinator.com/item?id=48979413

Points: 1

# Comments: 0

Article URL: https://pyrefly.org/blog/pyrefly-attrs/

Comments URL: https://news.ycombinator.com/item?id=48978616

Points: 1

# Comments: 0

Article URL: https://news.risky.biz/risky-bulletin-hacker-wipes-romanias-entire-land-registry-database/

Comments URL: https://news.ycombinator.com/item?id=48978605

Points: 1

# Comments: 0

Article URL: https://golemui.com/blog/the-age-of-token-efficiency/

Comments URL: https://news.ycombinator.com/item?id=48978587

Points: 1

# Comments: 0

This toy retro synth installs and runs on esp32-s3 or p4 as an ELF file under BreezyBox firmware. It uses my work-in-progress immediate mode TUI library in C.

Web-based version via emscripten: https://mini000.itch.io/beeper

Source code: https://github.com/valdanylchuk/beeper

Demo video running on Tanmatsu (esp32-p4): https://www.youtube.com/watch?v=uCH9XYZ7QB0

The main challenge I was going for was actually to find a nice minimal set of controls for a subtractive synth. Usually there are 40-50 of them in "simple" synths; I ended up with 16.

In terms of TUI, I wanted to see if it would be practical at embedded C scale to use a modern approach similar to ratatui, Dear ImGui, or Nuklear. I am quite happy with it so far. Will keep using and extending it for more embedded apps.

I did not publish the library as a separate project yet; it is not mature enough. Feel free to reuse it via good old copying for now if you like it.

For those who remember my earlier post here about the BreezyBox ( https://news.ycombinator.com/item?id=46918429 ), a few updates from the last 6 months:

* added sshd for inbound connections * extended shell with variables, loops, conditions, tar, grep, diff * added sound component, including pico8-themed sequencer, and a few example apps * ported my self-hosting C compiler for RISC-V (rcc700) * shared a full example firmware project for Tanmatsu (p4), in addition to earlier Waveshare (s3)

Thanks for checking this out! I welcome any questions and feedback about the synth app, the TUI library, or the Breezybox firmware.

Comments URL: https://news.ycombinator.com/item?id=48978565

Points: 1

# Comments: 0

Article URL: https://kotaku.com/gta-vice-city-made-playable-in-gta-3-made-playable-in-gta-san-andreas-2000717510

Comments URL: https://news.ycombinator.com/item?id=48978545

Points: 1

# Comments: 0

Hi HN,

I've been working on QUBE Predict, a cloud platform for drug response prediction built around graph-based biological descriptor learning.

Rather than relying solely on end-to-end models, the framework focuses on reusable biological descriptor representations that can be paired with conventional machine learning algorithms.

The platform includes:

• REST API

• Web interface

• Batch prediction

• Authentication and API keys

• Cloud deployment

The methodology has been described in a publicly available technical paper.

Paper:

https://doi.org/10.5281/zenodo.21455765

Live Demo:

https://qube-predict.streamlit.app/

GitHub:

https://github.com/gulfamh1999-afk/qube-predict-ui

I'd really appreciate technical feedback, especially on the representation-learning methodology and platform architecture.

Comments URL: https://news.ycombinator.com/item?id=48978516

Points: 1

# Comments: 0

Article URL: https://ieeexplore.ieee.org/document/11196478

Comments URL: https://news.ycombinator.com/item?id=48978508

Points: 1

# Comments: 0

Article URL: https://blog.brendankeaton.com/self-service-ransomware-as-security-against-local-ai-tools

Comments URL: https://news.ycombinator.com/item?id=48978503

Points: 1

# Comments: 1

Article URL: https://programmingsimplicity.substack.com/p/languages-that-stretch-your-di-capabilities

Comments URL: https://news.ycombinator.com/item?id=48978497

Points: 1

# Comments: 0

Article URL: https://duely-44998.bubbleapps.io/

Comments URL: https://news.ycombinator.com/item?id=48978454

Points: 2

# Comments: 1